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Found 10 Skills
Expert in Spring Data Neo4j integration patterns for graph database development. Use when working with Neo4j graph databases, node entities, relationships, Cypher queries, reactive Neo4j operations, or Spring Data Neo4j repositories. Essential for graph data modeling, relationship mapping, custom queries, and Neo4j testing strategies.
Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.
Comprehensive guide for writing modern Neo4j Cypher read queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, and Quantified Path Patterns (QPP) for efficient graph traversal.
Fully local multi-agent swarm intelligence simulation engine using Neo4j + Ollama for public opinion, market sentiment, and social dynamics prediction.
Knowledge graph specialist for entity and causal relationship modelingUse when "knowledge graph, graph database, falkordb, neo4j, cypher query, entity resolution, causal relationships, graph traversal, graph-database, knowledge-graph, falkordb, neo4j, cypher, entity-resolution, causal-graph, ml-memory" mentioned.
Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
Use Neo4j memory MCP for creating/updating linked memories (entities, relations), de-duplication (DRY), and retrieval queries for project continuity. Use when saving global learnings or querying graph relationships.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Discovers, tests, and manages remote SSH infrastructure hosts and Docker services across 5 hosts (infra.local, deus, homeassistant, pi4-motor, armitage). Use when checking infrastructure status, verifying service connectivity, managing Docker containers, troubleshooting remote services, or before using remote resources (MongoDB, Langfuse, OTLP, Neo4j). Triggers on "check infrastructure", "connect to infra/deus/ha", "test MongoDB on infra", "view Docker services", "verify connectivity", "troubleshoot remote service", "what services are running", or when remote connections fail.
Creates repository following Clean Architecture with Protocol in domain layer and Implementation in infrastructure layer. Use when adding new data access layer, creating database interaction, implementing persistence, or need to store/retrieve domain models. Enforces Protocol/ABC pattern with ServiceResult, ManagedResource lifecycle, and proper layer separation. Triggers on "create repository for X", "implement data access for Y", "add persistence layer", or "store/retrieve domain model".